Wearable Patch-Based Estimation of Oxygen Uptake and Assessment of Clinical Status during Cardiopulmonary Exercise Testing in Patients With Heart Failure

JOURNAL OF CARDIAC FAILURE

卷:26

Article

Shandhi, Md Mobashir Hasan¹  Hersek, Sinan¹  Fan, Joanna²  Sander, Erica²  De Marco, Teresa²  Heller, J. Alex³  Etemadi, Mozziyar³  Klein, Liviu²  Inan, Omer T.¹ 

[1] Georgia Inst Technol, Dept ECE, Atlanta, GA USA

[2] Univ Calif San Francisco, Sch Med, San Francisco, CA USA

[3] Northwestern Univ, Sch Med, Chicago, IL USA

关键词: Cardiopulmonary exercise test;    Cardiovascular monitoring;    Heart failure;    Seismocardiogram;    Wearable sensor;   

DOI  :  10.1016/j.cardfail.2020.05.014

来源: Elsevier

【 摘 要 】

Background: To estimate oxygen uptake (VO2) from cardiopulmonary exercise testing (CPX) using simultaneously recorded seismocardiogram (SCG) and electrocardiogram (ECG) signals captured with a small wearable patch. CPX is an important risk stratification tool for patients with heart failure (HF) owing to the prognostic value of the features derived from the gas exchange variables such as VO2. However, CPX requires specialized equipment, as well as trained professionals to conduct the study. Methods and Results: We have conducted a total of 68 CPX tests on 59 patients with HF with reduced ejection fraction (31% women, mean age 55 13 years, ejection fraction 0.27 +/- 0.11, 79% stage C). The patients were fitted with a wearable sensing patch and underwent treadmill CPX. We divided the dataset into a training-testing set (n = 44) anda separate validation set (n = 24). We developed globalized (population) regression models to estimate VO2 from the SCG and ECG signals measured continuously with the patch. We further classified the patients as stage D or C using the SCG and ECG features to assess the ability to detect clinical state from the wearable patch measurements alone. We developed the regression and classification model with cross-validation on the training-testing set and validated the models on the validation set. The regression model to estimate VO2 from the wearable features yielded a moderate correlation (R-2 of 0.64) with a root mean square error of 2.51 +/- 1.12 mL . kg-1 . min(-1) on the training-testing set, whereas R-2 and root mean square error on the validation set were 0.76 and 2.28 +/- 0.93 mL . kg(-1) . min(-1), respectively. Furthermore, the classification of clinical state yielded accuracy, sensitivity, specificity, and an area under the receiver operating characteristic curve values of 0.84, 0.91, 0.64, and 0.74, respectively, for the training-testing set, and 0.83, 0.86, 0.67, and 0.92, respectively, for the validation set. Conclusions: Wearable SCG and ECG can assess CPX VO2 and thereby classify clinical status for patients with HF. These methods may provide value in the risk stratification of patients with HF by tracking cardiopulmonary parameters and clinical status outside of specialized settings, potentially allowing for more frequent assessments to be performed during longitudinal monitoring and treatment.

【 授权许可】

Free